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http://dx.doi.org/10.14400/JDC.2016.14.3.165

Differences in Sentiment on SNS: Comparison among Six Languages  

Kim, Hyung-Ho (Dept. of Information & Logistics of Sehan University)
Jang, Phil-Sik (Dept. of Information & Logistics of Sehan University)
Publication Information
Journal of Digital Convergence / v.14, no.3, 2016 , pp. 165-170 More about this Journal
Abstract
The purpose of this study was to explore the differences in sentiment on social networking sites among six languages (English, German, Russian, Spanish, Turkish and Dutch). A total of 204 million tweets were collected using Streaming API. Subjective/objective ratio, sentiment strength, positive/negative ratio, number of retweets and boundary impermeability were analyzed with SentiStrength to estimate the trends of emotional expression via Twitter. The results showed that subjective/objective ratio and the positive/negative ratio of tweets were significantly different by languages (p<0.001). And, there were significant effects of language on sentiment strength, boundary impermeability and the number of retweets (p<0.001). The results also indicate that the cross-cultural, language differences should be taken into account in sentiment analysis on SNS.
Keywords
SNS; Sentiment Analysis; Twitter; Cultural Difference; Sentiment Strength;
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Times Cited By KSCI : 4  (Citation Analysis)
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1 K. Choi, J. A. Yoo, "A reviews on the social network analysis using R", Journal of the Korea Convergence Society, Vol. 6, No. 1, pp. 77-83, 2015.   DOI
2 J. Y. Go, K. H. Lee, "SNS disclosure of personal information in M2M environment threats and countermeasures", Journal of the Korea Convergence Society, Vol. 5, No. 1, pp. 29-34, 2014.   DOI
3 B. Pang, and L. Lee, "Opinion mining and sentiment analysis", Foundations and Trends in Information Retrieval, Vol. 1. No. 2, pp. 1-135, 2008.
4 P. S. Jang, "Study on principal sentiment analysis of social data", Journal of The Korea Society of Computer and Information, Vol. 19, No. 12, pp.49-56, 2014.
5 D. M. Boyd and N. B. Ellison, N. B. "Social network sites: Definition, history, and scholarship", Journal of Computer-Mediated Communication, Vol. 13, No. 1. pp. 210-230, 2007   DOI
6 J. J. Yoo, D. Kim and J. Moon, "Exploring cross-cultural differences in self-presentation and self-disclosure in social Networking sites: A comparison of korean and american SNS users", Journal of Advertising and Promotion Research, Vol. 1, No. 2, pp. 77-118, 2012.
7 S. Luo, "Cross-cultural differences between American and Chinese college students on self-disclosure on social media. Graduate Theses and Dissertations", p.1-70, Iowa State University. 2014.
8 K. Omori and M. Allen, "Cultural differences between american and japanese self-presentation on SNSs", International Journal of Interactive Communication Systems and Technologies (IJICST), Vol. 4, Issue 1. DOI: 10.4018/ijicst.2014010104, 2014.   DOI
9 S. A. Golder and M. W. Macy, "Diurnal and seasonal mood vary with work, sleep and daylength across diverse cultures." Science, 333, pp. 1878-1881, 2011.   DOI
10 J. Y. Lee, P. S. Jang, "Effects of message polarity and type on word of mouth through SNS (Social Network Service)", The Journal of Digital Policy & Management, Vol. 11, No. 6, pp. 129-135, 2013.
11 S. C. Walton and R. E. Rice, "Mediated disclosure on Twitter: The roles of gender and identity in boundary impermeability, valence, disclosure, and stage", Computers in Human Behavior, Vol. 29, pp. 1465-1474, 2013.   DOI
12 "The Streaming APIs", (C) 2016 Twitter, Inc., https://dev.twitter.com/streaming/overview (Jan 5, 2016)
13 "SentiStrength", http://sentistrength.wlv.ac.uk/, (Jan 25, 2016)
14 "MongoDB 3.2", MongoDB, Inc., https://www.mongodb.org/ (Jan 5, 2016)
15 M. Thelwall, K. Buckley. & G. Paltoglou, "Sentiment strength detection for the social Web", Journal of the American Society for Information Science and Technology, Vol. 63, No. 1, pp. 163-173, 2012.   DOI